No evidence for visuomotor priming in a visually-guided action task
Bibliographic record
Abstract
Craighero et al. (Craighero, L., Fadiga, U., UmiltC.A., & Rizzolatti, G. (1996). Evidence for visuomotor priming effect. Neuroreport, 8, 347-349) showed that grasping movements were initiated more quickly when the goal object shared the same orientation as a previously seen 'prime' object. Because the goal object was never visible in these experiments, however, it is unclear whether the data should be construed as evidence for a general visuomotor priming effect (as the authors contend), or only as evidence for a more specific priming effect on memory-guided actions. In Experiment 1, we demonstrated that memory-guided but not visually guided grasping can be primed by passive viewing of a prime object. In Experiment 2, we compared the effects of a prime object on the grasping and naming of a visible target object. Participants were faster to name the target when its shape was the same as the prime, consistent with well-established perceptual priming effects. Under the identical set of testing parameters, however, reaction time for grasping was unaffected by the orientation or the shape of the prime. In Experiment 3, participants grasped the goal object after either viewing or grasping a prime object. Reaction time for grasping was unaffected by the visual features of the prime in both tasks. Taken together, these results are consistent with the view that perceptual memory - which presumably underlies visual priming effects - is largely irrelevant for programming the metrics of actions to visible objects. Visually guided actions are programmed in real-time by dedicated visuomotor modules that appear to be insensitive to the priming effects that are a hallmark of visual perception. © 2004 Elsevier Ltd. All rights reserved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".